Unmanned aerial vehicles (UAVs) are being used in a variety of situations where the risk to the pilot of a manned vehicle is high, such as in military operations. UAVs often operate in swarms in which a large number of agents perform a simple set of tasks to accomplish a larger goal. Swarms are well suited to perform missions having a search stage where multiple entities must be located over a wide geographic area. The simplified tasks performed by each agent in the swarm allow a greater number of agents to be controlled and monitored by fewer human operators. Pheromone maps are a common coordination mechanism between agents that emulate insect foraging behavior. Insects mark their environment with pheromones to leave messages for other insects. A digital pheromone map overlays a digital grid onto a geographic area. Similar to insects, agents leave digital information for other agents in each cell of the digital grid. The digital pheromone map acts as an action selection input for the next movement for each agent.
Digital pheromone map systems rely on each agent having a relatively accurate copy of the map and may utilize either a central map that all agents connect to or a distributed map maintained by each agent. Centralized map systems are susceptible to network outages, such as those caused by the agent moving out of range or active jamming of the map signal. However, distributed map systems require a sophisticated synchronization strategy to ensure that each agent has an up-to-date map to conduct an effective search.
Existing swarming search strategies conduct searches based on moving agents using a pheromone gradient from areas of high pheromone concentration (i.e., a high number of searched cells) to areas of lower pheromone concentration (i.e., a low number of searched cells). Such an approach is subject to local minima in which an agent is in an area of low pheromone concentration surrounded by areas of high pheromone concentration. This causes the agent to unnecessarily search the local minima while ignoring faraway unsearched areas. Typical swarm search strategies also do not optimize the search coverage of each agent while maximizing the coverage of the network created by the distributed agents. A poorly distributed network of agents makes the search system more susceptible to agent attrition, such as when an agent malfunctions or is destroyed by an enemy. Finally, updates between distributed agents are often inefficient because they require unnecessary synchronization between agents that do not have relevant digital pheromone map differences.